RLS adaptive blind beamforming algorithm for cyclostationarysignals

A new cost function, which is a modification of the cost function of Castedo and Figugiras-Vidal (1995) for the adaptive blind beamforming of cyclostationary signals, is proposed. The proposed cost function enables the well-known recursive least-squares technique to be applied. Simulations demonstrate that the resulting algorithm has a faster convergence speed than the stochastic gradient-based algorithm of Castedo and Figugiras-Vidal     

The fast subsampled-updating fast transversal filter (FSU FTF) RLS algorithm

We present a new fast algorithm for Recursive Least-Squares(rls) adaptive filtering that uses displacement structure and subsampled updating. Thefsu ftf algorithm is based on the Fast Transversal Filter(ftf) algorithm, which exploits the shift invariance that is present in therls adaptation of afir filter. Theftf algorithm is in essence the application of a rotation matrix to a set of filters and in that respect resembles the Levinson algorithm. In the subsampled updating approach, we accumulate the rotation matrices over some time interval before applying them to the filters. It turns out that the successive rotation matrices themselves can be obtained from a Schur type algorithm which, once properly initialized, does not require inner products. The various convolutions that thus appear in the algorithm are done using the Fast Fourier Transform(fft). For relatively long filters, the computational complexity of the new algorithm is smaller than the one of the well-known lms algorithm, rendering it especially suitable for applications such as acoustic echo cancellation.     

Online blind equalisation algorithm of an FIR MIMO channel system for non-stationary signals

Blind equalisation of an FIR multi-input multi-output channel system is an important task for numerous applications such as speech separation, de-reverberation, communication, signal processing and control, etc. In this paper, a cost function with the knowledge of correlation is reconstructed and a new online algorithm derived with a natural gradient search method for blind source separation of convolutional mixtures. Its implementation is simple and practical. Furthermore, the equivariance property is possessed by the algorithm. Simulations indicate the ability of the algorithm to perform blind equalisation under the weaker condition (the FIR system is equalisable) and also to make speech separation and de-reverberation simultaneous.     

Combination of Kalman filter and constant modulus algorithm withvariable stepsize for the equalisation of fast-fading channels

In fast-fading channels, the constant modulus algorithm (CMA) is unable to properly track the time-variations because the magnitude of the received signal changes too rapidly. The Kalman filter (KF), however, works well in time-varying channels but needs a training sequence to operate. Therefore, a combined CMA and KF algorithm is proposed in order to utilise the advantages of both algorithms. The associated step sizes of the CMA and the KF algorithm are also varied in accordance with the magnitude of the output. Simulations are presented to demonstrate the potential of the combination     

Low complexity concurrent constant modulus algorithm and soft decision directed scheme for blind equalisation

The recently introduced concurrent constant modulus algorithm (CMA) and decision-directed (DD) scheme provides a state-of-the-art low-complexity blind equalisation technique for high-order quadrature amplitude modulation (QAM) channels. At a small cost of slightly more than doubling the complexity of the standard CMA blind equaliser, this concurrent CMA and DD blind equaliser achieves a dramatic improvement in equalisation performance over the CMA. In the paper, a new blind equalisation scheme is proposed based on concurrent CMA and a novel soft decision-directed (SDD) adaptation. The proposed concurrent CMA and SDD blind equaliser has simpler computational requirements than the concurrent CMA and DD algorithm. Extensive simulation shows that it has the same steady-state equalisation performance as the concurrent CMA and DD algorithm and a faster convergence speed over the latter scheme     

A fast linear programming algorithm for blind equalization

A fast implementation of a special non-MSE cost function for blind equalization is presented here. This baud-rate equalization algorithm is based on a convex cost function coupled with a simple linear constraint on the equalizer parameters. For a generic class of channels with persistently exciting quadrature amplitude modulation input signals, this new algorithm allows the convergence of equalizer parameters to a unique global minimum achieving intersymbol interference suppression and carrier phase recovery     

一种RS码快速盲识别方法

提出了一种RS码的快速盲识别方法.该方法基于RS码的等效二进制分组码的循环移位特性,通过欧几里德算法计算循环移位前后码字的最大公约式,根据最大公约式指数的相关性来估计码长,并快速剔除含错码字,进而利用伽罗华域的傅里叶变换(Galois Field Fourier Transform,GFFT)实现RS码的本原多项式和生...     

Bayesian blind and semi-blind equalisation of channels with Markovinputs

An original full Bayesian approach is developed for blind and semi-blind equalisation of fading channels with Markov inputs. The sequence of discrete symbols is estimated according to a marginal maximum a posteriori criterion; the other unknown parameters are regarded as random nuisance parameters and are integrated out analytically. A batch algorithm is proposed to maximise the marginal posterior distribution. Simulation results are presented to demonstrate the effectiveness of the method     

一种短波通信盲信道快速分离算法

在短波通信中针对盲信道个数不恒定、信道频谱理论上不重叠的盲信道分离问题,基于小波多分辨率分析技术提出一种盲信道快速分离算法,并对实测短波通信信号进行盲信道快速分离的Monte Carlo试验。仿真结果表明,本文所提出的短波盲信道快速分析算法的分离精度和信道正确分离率都较高、抗噪声干扰能力强。     

一种快速的多目标跟踪非线性滤波算法

多机动目标跟踪问题是目前目标跟踪领域的一个重要研究方向,而数据关联与跟踪维持是多目标跟踪的核心部分。利用支持向量机在分类识别方面的优势,研究了基于支持向量机的数据关联方法。在此基础上,采用交互式多模型算法和无味卡尔曼滤波相结合的方法研究了多机动目标的跟踪问题。在该方法中,目标的运动状态和方位误差由选定的采样点来近似,在每个更新过程中,采样点随着状态方程传播并随非线性测量方程变换,得到目标的运动状态和方位误差的均值,避免了对非线性方程的线性化,至少给出最佳估计的二阶近似。与传统的扩展卡尔曼(EKF)方法进行了仿真比较,仿真结果表明了该算法的有效性。     

A numerically stable fast RLS algorithm for adaptive filtering andprediction based on the UD factorization

The use of UD factorization in adaptive RLS algorithms is interesting for its numeric robustness and because no square-root operations at all are involved. We describe a square root free fast RLS algorithm based on the UD factorization of the autocorrelation matrix. Numerous finite precision simulations tend to indicate that this algorithm is numerically stable. The algorithm requires 𝒪(𝒩) operations, where 𝒩 is the linear filter order     

Fast subsampled-updating stabilized fast transversal filter (FSUSFTF) RLS algorithm for adaptive filtering

We present a new, doubly fast algorithm for recursive least-squares (RLS) adaptive filtering that uses displacement structure and subsampled-updating. The fast subsampled-updating stabilized fast transversal filter (FSU SFTF) algorithm is mathematically equivalent to the classical fast transversal filter (FTF) algorithm. The FTF algorithm exploits the shift invariance that is present in the RLS adaptation of an FIR filter. The FTF algorithm is in essence the application of a rotation matrix to a set of filters and in that respect resembles the Levinson (1947) algorithm. In the subsampled-updating approach, we accumulate the rotation matrices over some time interval before applying them to the filters. It turns out that the successive rotation matrices themselves can be obtained from a Schur-type algorithm that, once properly initialized, does not require inner products. The various convolutions that appear In the algorithm are done using the fast Fourier transform (FFT). The resulting algorithm is doubly fast since it exploits FTF and FFTs. The roundoff error propagation in the FSU SFTF algorithm is identical to that in the SFTF algorithm: a numerically stabilized version of the classical FTF algorithm. The roundoff error generation, on the other hand, seems somewhat smaller. For relatively long filters, the computational complexity of the new algorithm is smaller than that of the well-known LMS algorithm, rendering it especially suitable for applications such as acoustic echo cancellation     

Recursive blind identification of non-Gaussian time-varying ARmodel and application to blind equalisation of time-varying channel

A novel method for the blind identification of a non-Gaussian time-varying autoregressive model is presented. By approximating the non-Gaussian probability density function of the model driving noise sequence with a Gaussian-mixture density, a pseudo maximum-likelihood estimation algorithm is proposed for model parameter estimation. The real model identification is then converted to a recursive least squares estimation of the model time-varying parameters and an inference of the Gaussian-mixture parameters, so that the entire identification algorithm can be recursively performed. As an important application, the proposed algorithm is applied to the problem of blind equalisation of a time-varying AR communication channel online. Simulation results show that the new blind equalisation algorithm can achieve accurate channel estimation and input symbol recovery     

Exploiting non-Gaussianity in blind identification and equalisation of MIMO FIR channels

The problem of blind identification and equalisation (BIE) of finite impulse response (FIR) channels in multiuser digital communications is investigated. The non-Gaussian nature and statistical independence of the users' data streams is exploited by resorting to blind signal separation (BSS) based on higher-order statistics (HOS). Two such techniques are put forward. The first technique is composed of an extension to the multiuser case of a second-order BIE method, followed by a BSS-based space-equalisation step. The second technique achieves joint space-time equalisation through the direct application of a HOS-based BSS method followed by a blind identification algorithm. In a number of numerical experiments, the first procedure proves less costly and more effective for short data records. Despite their computational complexity, interesting features such as constellation-independent channel identification and symbol recovery, and robustness to ill-conditioned channels in high SNR environments render HOS-BSS based BIE methods an effective alternative to BIE techniques exploiting other spatio-temporal structures.     

POCS超分辨率图像重构的快速算法

超分辨率图像重构是将多帧低分辨率图像重构成一幅高分辨率图像的过程。由于其求解是一大型病态求逆问题，计算量随着放大倍数的增加而急剧上升，如何降低计算复杂度是超分辨率成像所面临的一个急需解决的课题。提出了一个基于PoCs的高分辨率图像重构的快速算法。其原理是利用各低分辨率图像之间位移的关系将所有的低分辨率图像进行重组，然后对每个组进行PoCs超分辨图象重构。实验结果表明。该快速算法较大地提高了超分辨图像重构的速度。     

利用盲源分离算法实现DOA估计

提出一种基于快速盲源分离算法实现波达方向（DOA）估计的方法。构造了具有对角化结构的相关矩阵组,引入解盲源分离问题的联合对角化代价函数,采用一种快速的复数域乘性迭代算法求解代价函数,得到混迭矩阵逆的估计,进而实现DOA估计。与同类算法相比,该算法具有更广的适用性和更精确的DOA估计性能。仿真实验结果验证了算法的快速收敛性和优越的估计性能。     

一种适用于多径衰落无线信道的快速盲均衡算法研究

在无线数字通信中,信道衰落和多途传播效应引起的码间干扰,严重影响了通信的有效性和可靠性,盲均衡是对抗码间干扰的有效方法。文章研究了一种适用于无线信道的快速盲均衡算法,并进行了计算机仿真。仿真结果表明,在算法剩余均方误差非常接近的情况下,该算法快于传统的常数模算法。     

基于卡尔曼算法的网络安全态势预测方法分析

近几年,网络信息技术发展迅速,并逐渐进入社会各行各业的发展中,不仅改变着人们的生产方式,同时也改变了人们的生活方式,给人们衣食住行带来了很大的方便。但是应该看到网络在给人们带来极大便利的同时也存在较大的安全隐患,威胁人们的隐私和网络安全。所以必须要对网络安全态势进行分析和预测,找到一种合理的算法进行态势分析,建立科学的网络安全态势模型,为网络管理者做出安全防护的决策提供有效信息,提高网络安全度。这里主要利用卡尔曼算法进行网络安全态势预测方法的分析,并分析该算法的优越性。     

Analysis of a stabilization technique for the fixed-pointprewindowed RLS algorithm

A stable finite precision recursive least squares (RLS) algorithm is derived for the prewindowed growing memory case (forgetting factor, λ=1). The prewindowed growing memory RLS algorithm diverges under fixed-point implementation. The random walk phenomenon due to roundoff errors in the weight update causes the divergence of the algorithm. To overcome this effect, these roundoff errors are modeled such that their effect is incorporated into the algorithm. The steady-state behavior of this new algorithm is analyzed, and it is shown that the divergence phenomenon is actually eliminated, and the new algorithm converges